Support Sufficiency as Consequence-Sensitive Compression in Belief Arbitration

This paper argues that evidential compression in AI systems must be consequence-sensitive, proposing a recurrent arbitration architecture that compresses hypothesis geometry into a support-aware control state. This process is regulated by consequence geometries and resource constraints to prevent the collapse of policy-relevant distinctions.